AI Article Synopsis
- * The study included 212 pregnant women screened across four centers in Taiwan from 2017 to 2020, measuring specific serum levels and using them to create algorithmic models.
- * Our models showed strong accuracy in predicting preeclampsia risk, achieving AUC values of around 0.848-0.852 with good sensitivity and specificity during both the training and validation phases.
Article Abstract
We aim to establish a prediction model for pregnancy outcomes through a combinatorial analysis of circulating biomarkers and maternal characteristics to effectively identify pregnant women with higher risks of preeclampsia in the first and third trimesters within the Asian population. A total of two hundred and twelve pregnant women were screened for preeclampsia through a multicenter study conducted in four recruiting centers in Taiwan from 2017 to 2020. In addition, serum levels of sFlt-1/PlGF ratio, miR-181a, miR-210 and miR-223 were measured and transformed into multiples of the median. We thus further developed statistically validated algorithmic models by designing combinations of different maternal characteristics and biomarker levels. Through the performance of the training cohort (0.848 AUC, 0.73−0.96 95% CI, 80% sensitivity, 85% specificity, p < 0.001) and the validation cohort (0.852 AUC, 0.74−0.98 95% CI, 75% sensitivity, 87% specificity, p < 0.001) from one hundred and fifty-two women with a combination of miR-210, miR-181a and BMI, we established a preeclampsia prediction model for the first trimester. We successfully identified pregnant women with higher risks of preeclampsia in the first and third trimesters in the Asian population using the established prediction models that utilized combinatorial analysis of circulating biomarkers and maternal characteristics.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9320107 | PMC |
| http://dx.doi.org/10.3390/diagnostics12071533 | DOI Listing |
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